Due to the computational bottleneck, the large computational resources and the time lag that physics-based models incur when making a forecast, efficient models based on deep neural networks represent a promising alternative framework for weather modelling [1]}, [2]}. Instead of explicitly simulating the physics of the atmosphere,
